Wi-Fi Based Retail Analytics For Improved Customer Experience
Overview of the Project
A Wi-Fi tracking based retail analytics is used to localize and track customer journey data with the help of passive network sniffers. Sniffers are small Wi-Fi like devices used to capture packets of data across a network. Measured packets of data (probe requests) are used to identify the zone with the help of mac addresses where the Wi-Fi device is located. It provides information related to footfall analytics which allow retailers to make smarter decisions and manage their systems more efficiently. A challenge for a retailer is to know when and how to adapt to the needs of customers. Understanding customers’ behavior through physical analytics can provide crucial insights to the business owner in terms of effectiveness of promotions, staff allocation and efficiency of services. By further looking into customer dwell(stay) time, footfall data gives intuition into customer experience and satisfaction. The field of retail analysis has gone beyond ordinary data analysis or simple counting, now the world is using data mining and data discovery to produce applicable business intelligent insights.
Background
Footfall retail data and indoor location finding by using WI FI is considered as most emerging technology in tracking industry. The main inspiration of this project is the passive localization and tracking of customer in indoor environments using Probe Requests send by customers’ smartphones. These probe requests are later filtered to get MAC Address of devices and used for device tracking. Indoor customer localization enables retailers to know about customer traffic surrounding a store and converting it into in-store visits. Real time retail analytics will allow retailers to make operational and strategic decisions that will optimize staff, and improve merchandising and store displays. The exact location of a customer can be found out by using localization algorithm with the help of RSSI (Received Signal Strength Indication). The system will provide an insight into customer behavior, shopping patterns and dwell time inside a building.
Scope of the Project
Since a positive customer experience not only results in making the customers happy, but it also leads to addition revenue which in turn is beneficial for the retailers. Considering this factor, our project is typically focusing on retail stores for improving customer experience. Wi-Fi based retail analytics will provide visual customer location and detailed analysis of customer visitation which is important for retail stores to cope up with the competitive environment. This system will provide benefits to multiple teams simultaneously including marketing, merchandizing and IT. Other than retail stores, this system can be used to observe the visitor traffic across the industries including Automotive, Transport, Hospitality, Tourism, Property, Banking and Airports.
Target Audience
For this project, we will be focusing on creating a product based on retail analytics. Therefore, our targeted audience includes retailers who wants to monitor customer behavior for improving services and customer experience.
Possible Applications of Work
This system will provide benefits to multiple teams simultaneously including marketing, merchandizing and IT. We are using this system in retail but other possible applications to observe the visitors traffic across the industries includes Automotive, Transport, Hospitality, Tourism, Real Estate, Banking and Airports.
Existing System
Comparisons of Existing Systems
People counting and customer tracking gives an estimate about how much traffic surrounds a location and an idea about the peak time too. It is an important metric to measure performance in retails. So many companies are already working in this domain.
One of them is Shopper
Track Retail Traffic Solution. It is an incorporating broader market benchmark, promotional and other data sets, by which retailers will be able to convert real time data into meaningful insight to sell more and faster. But there is an issue, first the retailer has to convince the customer to install the mobile application to track customer on one’s way which is not an easy task. Another one is IPSOS Retail Performance providing the services of people counting and footfall solutions since 1989. It has become the foremost name in retail monitoring technology.
Therefore, it is a very expensive system and an average store owner cannot afford it. BLIX Traffic is also a real time footfall traffic analytics company for unparalleled insight into customer engagement and behavior. It has a quick and easy setup, which is installed in less than an hour using the existing Wi-Fi network of store. BLIX Traffic collects anonymous customer data from Wi-Fi enabled smartphones without depending upon any mobile application to be installed first. V Count is the another leading manufacturer of 3D people counting and customer counter, retail analytics system, visitor counter, people counter for stores.
V Count is used in more than 100+ countries and installed in over 25,000 points around the world. It captures the complete customer journey from entrance to exit but it depends on sensors like cameras and proximity sensors etc. , which makes the installation difficult and costly. Qminder is another customer service and a queue management system that counts the number of people at a location over a period of time. This system queues visitors by name which allows employees to approach visitors. It gathers data to improve staff planning and customer experience. It only offers services for queue management and solve the problem of waiting line. No localization and tracking experience is provided to the customer.
Purple Wi-Fi Analytics builds detailed customer profiles and better understanding about how customers are behaving but it’s about website analytics. Their platform provides real-time customer data and insight including name, age, gender, social interests, contact and location etc. , which is disliked by customers due to privacy issues.
Whereas our system¬¬¬ will possess a quick and easy setup, offers no interruption to store activities, measures both in-store and passing traffic outside the store. Most of the solutions available in market locates the target through active positioning system. But we are tracking target through passive positioning, which means that it is not necessary for the target to stay in active contact with the management station (via an application/tag). The only requirement is that Wi-Fi of the target device should be enabled. Moreover, most solutions of passive systems are based on the triangulation and multiple points positioning technology, here we are using Fingerprint-Based positioning technique which can locate the target position more effectively.
Drawbacks of Existing Systems
The following pointers provide some useful insights into the drawbacks of existing systems: Most of the existing systems perform active customer tracking which makes the system dependent on a mobile application, and making up a customer’s mind to install the mobile application is an uphill struggle. Most of the existing systems use a complicated hardware setup which is difficult for the store owners to install and maintain. Some existing systems are so costly that an average store owner cannot afford. Most existing systems use sensors like cameras etc. , and their installation is a challenge for retailers as it delays business activities. Some systems capture customer’s personal information like name, age, gender, and whereabouts etc. , which undertake privacy issues. Some of the systems use GPS location tracking which is only suitable for outdoor people tracking making it inaccurate for indoor environments. Some existing systems use Bluetooth technology to track customers which is less efficient than Wi-Fi technology.
Problem Statement
To provide a system for retailers to track customer journey and provides insight into customer counts, customer shopping patterns, dwell time, customer loyalty, bounce rates and much more to improve customer engagement and retail store performance.
Proposed System
Our system It is designed to perform Indoor localization and performs passive localization using fingerprinting which is efficient and easy to use as no installation is required. Furthermore, it provides less costly solution comparative to the existing systems.
Feasibility Study
Technical Feasibility
Wi-Fi Based Retail Analytics System for Improved Customer Experience is a complete Web Portal along with a Hardware module. The proposed system will work in two phases; offline and online phase. The offline phase involves creating a radio map. The radio map will store distributions of RSS values from all detected APs at specific points which are known as marking positions. The marking positions together with the MAC address of each detected APs and their corresponding RSS values will be stored in the database to create the radio map. In the online phase, when the user enables Wi-Fi of smartphone, the sniffer component will collect sample of received signal strength (RSS) values from all detected APs. The data collected will be compared in the radio map to estimate the position of the user.
The main technologies and tools associated with this system are:
- A web portal which requires HTML, CSS, Bootstrap, and CodeIgniter Web Framework of PHP.
- Diagram and Gantt chart drawing tools such as Microsoft Visio, Microsoft Project Professional etc.
- A centralized database to create fingerprint (samples at specific location) i. e. MySQL.
- A data analytics engine developed using machine learning techniques to analyze all the data.
- A hardware module which is Raspberry–Pi including the WLAN USB Stick for catching Wi-Fi probe request.
- A tool for technical documentation.
Each of these technologies and tools are easily available and all the required technical expertise are manageable. Time limits and Implementation of these techniques are synchronized. Hence, it is clear that this system is technically feasible.
Operational Feasibility
The Wi-Fi tracking system is an effective alternative to Bluetooth and GPS for indoor localization environments. However, these systems are not suitable for positioning inside buildings due to a high level of signal degradation. Wi-Fi devices can communicate up to 11mbps which is 11 times faster than Bluetooth. The motivation for doing this project was to make an attempt to solve a challenging problem and convert it into a commercial product. Using Wi-Fi finger print algorithms for localization is a popular method since it uses a network of Access Points (AP) that nowadays are widely deployed and available at low cost. The measured Received Signal Strength (RSS) is translated into distance using a propagation model or compared to calibrated RSS maps, known as RSS fingerprints.
This Wi-Fi tracking system for device localization is feasible for both business and operational perspectives. On the operational side, this project proposes a strategy for analyzing the performance of the Wi-Fi tracking systems by looking at the most important statistics both on individual and aggregated levels. From a business perspective, this Wi-Fi based tracking system is clearly based on the framework for indoor localization fingerprint algorithm. Our project is typically focusing on retail stores for improving customer experience. Wi-Fi based retail analytics will provide visual customer location and detailed analysis of customer visitation which is important for retail stores to cope up with the competitive environment. This system will provide benefits to multiple teams simultaneously including marketing, merchandizing and IT.
Economical Feasibility
Economically is not impossible. Its overcall costs worth One Lac, Twenty Thousand Pakistani rupees which includes all hardware and software expenses according to requirements.
Following are the benefits when operational:
- Customer Behavior Insights: The first and foremost advantage of this system is that its offer tangible and actionable insights into customer behavior. From studying the social responses to a product to measuring how a campaign improved the store’s conversion rates, this system will give a highly accurate picture to retailers of what works and what doesn’t.
- Managing the Basics: This system will play a vital role in elevating the efficiencies in everyday business management. Our data analytics engine will allow the retailers to take swift actions for decision-making on stocking and tracking regularly.
- Optimizing In-Store Operations: This system will offer a profound understanding of the customer behavior inside the store. Tracing customer shopping patterns and dwelling times can unlock innumerable opportunities for all kinds of retail operations, from individual stores to sprawling shopping malls. With these metrics at hand, retailers can analyze the best staffing options, the most appealing design techniques and the most effective selling tactics.
- Probable Shift of Customer Loyalties: By giving meaningful insights into customer behavior, retail analytics helps in bolstering the relationship between a store and its visitors.
Limitation and Challenges in Implementation of Project Limitations:
Indoor Wi-Fi tracking system cannot achieve high-accuracy localization in crowded environments. It only can be used in indoor environments with fewer people, because of the complicated environment and the Wi-Fi signal strength is easily affected by obstacles, shadowing, speed and other factors. The sharply turning and variable motion also lead to large error distance. [endnoteRef: 8][8] [8: [8] https: //retailnext. net/en/blog/peeking-behind-the-wi-fi-curtain-the-limitations-of-wi-fi-mdd-as-traffic-counting-technologies/] In indoor environments, there is two dimensional model, which means we could not locate device in three dimensional for example we cannot differentiate between a device on the stairs and another device right under it on the ground floor. When the device is out of the range of the sniffer environment, the tracking system still estimate the device location based on the existing fingerprint database, which would lead to huge tracking error. A new algorithm to detect whether the device is out of range should be developed in the future. Currently the sniffers use the arrival time of the broadcasted packet to find out the time axis synchronization. However, the adjustment of time difference is not ideal. If the departure time of the packet is available, the tracking performance can be improved further. Our basic aim is to counting the traffic on retail store and converts this information to customer intelligence. Traffic counting is meant to give you accurate data so that you can compare traffic data with transactions and staffing allocation. But Wi-Fi data such as mac address and signal strength cannot be sufficient to use this way. It’s just not enough to calculate critical retail statistics like conversion rates and staff-to-customer ratio without using transactions record.
Challenges:
Any device’s data needed to have a timestamp and location associated with it. That location could be found if the data was collected from a known location (such as retail store). Identification of customers would be dependent on the amount of MAC addresses detected in the store and customers may tend to appear in the area in groups. If 500 MAC addresses are detected in an area while a single person appears in our secondary data set, it could take a significant amount of repeat locations of this customer from that 500 Mac addresses which belongs to one. By our system’s perception, the sniffing station is set-up in retail store to detect Wi-Fi devices. However, the real world conditions are complex. Wi-Fi enabled handsets send packets randomly in the store. Therefore, this system requires a mechanism to increase the packets captured efficiently and measured RSSI accurately.
Another key challenge is the association and processing of big data to get precise results. It would take four to five times more than are actually required for the data load. This, of course, makes the infrastructure more complex and increases the cost significantly. The system is related to the concerns over customer’s data privacy. Thus it will be a huge task of assuring end users that their personal information is safe. Depending on the technology we are using, we could get channel management problems especially if the Wi-Fi is 2. 4GHz based. This affects the Wi-Fi systems performance.
